library(GenomicAlignments)
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library(tidyverse)
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library(cqn)
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library(edgeR)
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library(ggplot2)
library(cowplot)
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colsBig <- clusterExperiment:::massivePalette
plotGCHex <- function(gr, counts){
counts2 <- counts
df <- as_tibble(cbind(counts2,gc=mcols(gr)$gc))
df <- gather(df, sample, value, -gc)
ggplot(data=df, aes(x=gc, y=log(value+1)) ) +
ylab("log(count + 1)") + xlab("GC-content") +
geom_hex(bins = 50) + theme_bw() #+ facet_wrap(~sample, nrow=2)
}
pal <- RColorBrewer::brewer.pal(n=8, "Dark2")
source("~/Dropbox/research/atacseq/method/gcqn_validated.R")
### this dataset combines samples from a number of different sources, therefore the GC effects are wildly different between samples.
data=read.delim("~/data/atac/calderon2019_GSE118189/GSE118189_ATAC_counts.txt.gz")
colnames(data) <- substr(colnames(data),2,nchar(colnames(data)))
# get GC content
rn <- rownames(data)
sn <- unlist(lapply(lapply(strsplit(rn,split="_"),"[",1),function(x) gsub(pattern="chr",x=x,replacement="")))
start <- as.numeric(unlist(lapply(strsplit(rn,split="_"),"[",2)))
end <- as.numeric(unlist(lapply(strsplit(rn,split="_"),"[",3)))
gr <- GRanges(seqnames=sn, ranges=IRanges(start, end), strand="*", mcols=data.frame(peakID=rn))
ff <- FaFile("~/data/genomes/human/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa")
peakSeqs <- getSeq(x=ff, gr)
gcContentPeaks <- letterFrequency(peakSeqs, "GC",as.prob=TRUE)[,1]
gcGroups <- Hmisc::cut2(gcContentPeaks, g=20)
mcols(gr)$gc <- gcContentPeaks
# get metadata
cnames <- colnames(data)
donor <- unlist(lapply(strsplit(cnames,split=".",fixed=TRUE),"[",1))
celltype <- factor(unlist(lapply(strsplit(cnames,split=".",fixed=TRUE),"[",2)))
condition <- unlist(lapply(strsplit(cnames,split=".",fixed=TRUE),"[",3))
table(celltype,condition)
## condition
## celltype S U
## Bulk_B 3 4
## CD8pos_T 3 4
## Central_memory_CD8pos_T 4 4
## Effector_CD4pos_T 3 4
## Effector_memory_CD8pos_T 4 4
## Follicular_T_Helper 4 5
## Gamma_delta_T 3 4
## Immature_NK 0 5
## Mature_NK 6 4
## Mem_B 4 4
## Memory_NK 0 6
## Memory_Teffs 4 4
## Memory_Tregs 4 4
## Monocytes 6 3
## Myeloid_DCs 0 3
## Naive_B 3 4
## Naive_CD8_T 4 4
## Naive_Teffs 5 4
## Naive_Tregs 2 2
## pDCs 0 3
## Plasmablasts 0 3
## Regulatory_T 4 4
## Th1_precursors 4 4
## Th17_precursors 4 3
## Th2_precursors 4 4
# QC measures
qcMeasures <- openxlsx::read.xlsx(xlsxFile = "~/data/atac/calderon2019_GSE118189/Supplementary_tables.xlsx",
sheet = 5)
qcMeasures <- qcMeasures[,1:4]
qcMeasures$sample <- gsub(x=qcMeasures$sample, pattern="-", replacement=".", fixed=TRUE)
rownames(qcMeasures) <- qcMeasures$sample
qcMeasures <- qcMeasures[colnames(data),]
# batch
addSamples <- read.table("~/data/atac/calderon2019_GSE118189/samples_with_additional_resequencing.txt",
stringsAsFactors = FALSE)[,1]
addSamples <- gsub(x=addSamples, pattern="-", replacement=".", fixed=TRUE)
batch2 <- rep(0, ncol(data))
names(batch2) <- colnames(data)
batch2[addSamples]<- 1
batch2 <- factor(batch2)
batch <- droplevels(interaction(donor, batch2))
table(celltype,condition, batch)
## , , batch = 1001.0
##
## condition
## celltype S U
## Bulk_B 1 1
## CD8pos_T 1 1
## Central_memory_CD8pos_T 1 1
## Effector_CD4pos_T 1 1
## Effector_memory_CD8pos_T 1 1
## Follicular_T_Helper 1 1
## Gamma_delta_T 0 1
## Immature_NK 0 1
## Mature_NK 1 1
## Mem_B 1 1
## Memory_NK 0 1
## Memory_Teffs 1 1
## Memory_Tregs 1 1
## Monocytes 1 1
## Myeloid_DCs 0 1
## Naive_B 1 1
## Naive_CD8_T 1 1
## Naive_Teffs 1 1
## Naive_Tregs 0 0
## pDCs 0 1
## Plasmablasts 0 1
## Regulatory_T 0 0
## Th1_precursors 1 1
## Th17_precursors 1 1
## Th2_precursors 1 1
##
## , , batch = 1002.0
##
## condition
## celltype S U
## Bulk_B 1 1
## CD8pos_T 1 1
## Central_memory_CD8pos_T 1 1
## Effector_CD4pos_T 1 1
## Effector_memory_CD8pos_T 1 1
## Follicular_T_Helper 1 1
## Gamma_delta_T 1 1
## Immature_NK 0 1
## Mature_NK 1 0
## Mem_B 1 1
## Memory_NK 0 1
## Memory_Teffs 1 1
## Memory_Tregs 1 1
## Monocytes 1 0
## Myeloid_DCs 0 1
## Naive_B 1 1
## Naive_CD8_T 1 1
## Naive_Teffs 1 1
## Naive_Tregs 0 0
## pDCs 0 1
## Plasmablasts 0 1
## Regulatory_T 1 1
## Th1_precursors 1 1
## Th17_precursors 1 1
## Th2_precursors 1 1
##
## , , batch = 1003.0
##
## condition
## celltype S U
## Bulk_B 0 0
## CD8pos_T 0 0
## Central_memory_CD8pos_T 0 1
## Effector_CD4pos_T 0 0
## Effector_memory_CD8pos_T 0 0
## Follicular_T_Helper 0 0
## Gamma_delta_T 0 0
## Immature_NK 0 1
## Mature_NK 0 1
## Mem_B 0 0
## Memory_NK 0 0
## Memory_Teffs 0 0
## Memory_Tregs 0 0
## Monocytes 0 1
## Myeloid_DCs 0 0
## Naive_B 0 1
## Naive_CD8_T 1 0
## Naive_Teffs 0 0
## Naive_Tregs 0 0
## pDCs 0 0
## Plasmablasts 0 0
## Regulatory_T 0 0
## Th1_precursors 0 1
## Th17_precursors 0 0
## Th2_precursors 0 0
##
## , , batch = 1004.0
##
## condition
## celltype S U
## Bulk_B 0 1
## CD8pos_T 0 1
## Central_memory_CD8pos_T 1 1
## Effector_CD4pos_T 0 1
## Effector_memory_CD8pos_T 1 1
## Follicular_T_Helper 1 1
## Gamma_delta_T 1 1
## Immature_NK 0 1
## Mature_NK 1 1
## Mem_B 0 1
## Memory_NK 0 1
## Memory_Teffs 1 1
## Memory_Tregs 1 1
## Monocytes 1 1
## Myeloid_DCs 0 0
## Naive_B 0 1
## Naive_CD8_T 1 1
## Naive_Teffs 1 1
## Naive_Tregs 1 1
## pDCs 0 0
## Plasmablasts 0 0
## Regulatory_T 1 1
## Th1_precursors 1 1
## Th17_precursors 1 1
## Th2_precursors 1 1
##
## , , batch = 1008.0
##
## condition
## celltype S U
## Bulk_B 0 0
## CD8pos_T 0 0
## Central_memory_CD8pos_T 0 0
## Effector_CD4pos_T 0 0
## Effector_memory_CD8pos_T 0 0
## Follicular_T_Helper 0 0
## Gamma_delta_T 0 0
## Immature_NK 0 1
## Mature_NK 1 1
## Mem_B 0 0
## Memory_NK 0 1
## Memory_Teffs 0 0
## Memory_Tregs 0 0
## Monocytes 1 0
## Myeloid_DCs 0 1
## Naive_B 0 0
## Naive_CD8_T 0 0
## Naive_Teffs 0 0
## Naive_Tregs 0 1
## pDCs 0 1
## Plasmablasts 0 0
## Regulatory_T 0 0
## Th1_precursors 0 0
## Th17_precursors 0 0
## Th2_precursors 0 0
##
## , , batch = 1010.0
##
## condition
## celltype S U
## Bulk_B 0 0
## CD8pos_T 0 0
## Central_memory_CD8pos_T 0 0
## Effector_CD4pos_T 0 0
## Effector_memory_CD8pos_T 0 0
## Follicular_T_Helper 0 1
## Gamma_delta_T 0 0
## Immature_NK 0 0
## Mature_NK 1 0
## Mem_B 1 0
## Memory_NK 0 1
## Memory_Teffs 0 0
## Memory_Tregs 0 0
## Monocytes 1 0
## Myeloid_DCs 0 0
## Naive_B 0 0
## Naive_CD8_T 0 0
## Naive_Teffs 0 0
## Naive_Tregs 1 0
## pDCs 0 0
## Plasmablasts 0 1
## Regulatory_T 0 0
## Th1_precursors 0 0
## Th17_precursors 0 0
## Th2_precursors 0 0
##
## , , batch = 1011.0
##
## condition
## celltype S U
## Bulk_B 0 0
## CD8pos_T 0 0
## Central_memory_CD8pos_T 0 0
## Effector_CD4pos_T 0 0
## Effector_memory_CD8pos_T 0 0
## Follicular_T_Helper 0 0
## Gamma_delta_T 0 0
## Immature_NK 0 0
## Mature_NK 0 0
## Mem_B 0 0
## Memory_NK 0 0
## Memory_Teffs 0 0
## Memory_Tregs 0 0
## Monocytes 0 0
## Myeloid_DCs 0 0
## Naive_B 0 0
## Naive_CD8_T 0 0
## Naive_Teffs 1 0
## Naive_Tregs 0 0
## pDCs 0 0
## Plasmablasts 0 0
## Regulatory_T 0 0
## Th1_precursors 0 0
## Th17_precursors 0 0
## Th2_precursors 0 0
##
## , , batch = 1001.1
##
## condition
## celltype S U
## Bulk_B 0 0
## CD8pos_T 0 0
## Central_memory_CD8pos_T 0 0
## Effector_CD4pos_T 0 0
## Effector_memory_CD8pos_T 0 0
## Follicular_T_Helper 0 0
## Gamma_delta_T 0 0
## Immature_NK 0 0
## Mature_NK 0 0
## Mem_B 0 0
## Memory_NK 0 0
## Memory_Teffs 0 0
## Memory_Tregs 0 0
## Monocytes 0 0
## Myeloid_DCs 0 0
## Naive_B 0 0
## Naive_CD8_T 0 0
## Naive_Teffs 0 0
## Naive_Tregs 0 0
## pDCs 0 0
## Plasmablasts 0 0
## Regulatory_T 1 1
## Th1_precursors 0 0
## Th17_precursors 0 0
## Th2_precursors 0 0
##
## , , batch = 1003.1
##
## condition
## celltype S U
## Bulk_B 1 1
## CD8pos_T 1 1
## Central_memory_CD8pos_T 1 0
## Effector_CD4pos_T 1 1
## Effector_memory_CD8pos_T 1 1
## Follicular_T_Helper 1 1
## Gamma_delta_T 1 1
## Immature_NK 0 0
## Mature_NK 1 0
## Mem_B 1 1
## Memory_NK 0 1
## Memory_Teffs 1 1
## Memory_Tregs 1 1
## Monocytes 1 0
## Myeloid_DCs 0 0
## Naive_B 1 0
## Naive_CD8_T 0 1
## Naive_Teffs 1 1
## Naive_Tregs 0 0
## pDCs 0 0
## Plasmablasts 0 0
## Regulatory_T 1 1
## Th1_precursors 1 0
## Th17_precursors 1 0
## Th2_precursors 1 1
counts <- as.matrix(data) ; rm(data) ; gc()
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 9677419 516.9 17820628 951.8 NA 17820628 951.8
## Vcells 169446367 1292.8 340237760 2595.9 16384 283464797 2162.7
Lowess fits
GC content
lowListGC <- list()
for(kk in 1:ncol(counts)){
set.seed(kk)
lowListGC[[kk]] <- lowess(x=gcContentPeaks, y=log1p(counts[,kk]), f=1/10)
}
# plot(x=seq(min(gcContentPeaks), max(gcContentPeaks), length=10),
# y=seq(0, 4, length=10), type='n',
# xlab="GC-content", ylab="Log(count + 1)")
# for(kk in 1:length(lowListGC)){
# # oogc <- order(gcContentPeaks)
# # selPeaks <- oogc[seq(1,length(oogc), length.out=1e4)]
# # oo <- order(lowList[[kk]]$x[selPeaks])
# # lines(x=lowList[[kk]]$x[selPeaks][oo], y=lowList[[kk]]$y[selPeaks][oo], col=kk)
# oo <- order(lowListGC[[kk]]$x)
# lines(x=lowListGC[[kk]]$x[oo], y=lowListGC[[kk]]$y[oo], col=kk)
# }
for(cc in 1:nlevels(celltype)){
curCT <- levels(celltype)[cc]
id <- which(celltype == curCT)
curBatch <- batch[id]
plot(x=seq(min(gcContentPeaks), max(gcContentPeaks), length=10),
y=seq(0, 4, length=10), type='n',
xlab="GC-content", ylab="Log(count + 1)", main=curCT)
for(ii in 1:length(id)){
curID <- id[ii]
oo <- order(lowListGC[[curID]]$x)
lines(x=lowListGC[[curID]]$x[oo], y=lowListGC[[curID]]$y[oo], col=colsBig[batch[curID]])
}
}
for(bb in 1:nlevels(batch)){
curB <- levels(batch)[bb]
id <- which(batch == curB)
plot(x=seq(min(gcContentPeaks), max(gcContentPeaks), length=10),
y=seq(0, 4, length=10), type='n',
xlab="GC-content", ylab="Log(count + 1)", main=curB)
for(ii in 1:length(id)){
curID <- id[ii]
oo <- order(lowListGC[[curID]]$x)
lines(x=lowListGC[[curID]]$x[oo], y=lowListGC[[curID]]$y[oo], col=colsBig[batch[curID]])
}
}
Visualization
lowMemNK <- lowListGC[celltype == "Memory_NK"]
dfList <- list()
for(ss in 1:length(lowMemNK)){
oox <- order(lowMemNK[[ss]]$x)
dfList[[ss]] <- data.frame(x=lowMemNK[[ss]]$x[oox], y=lowMemNK[[ss]]$y[oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
## association of GC content with counts
plotGCHex(gr, rowMeans(counts[, celltype == "Memory_NK"])) +
theme(axis.title = element_text(size=16)) +
labs(fill="Nr. of peaks") +
geom_line(aes(x=x, y=y, group=sample, color=sample), data=dfAll, size=1) +
scale_color_discrete()
## just the average GC content
p1 <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("GC-content") +
ylab("Log(count + 1)") +
theme_classic()
# rm(lowListGC) ; gc()
# across all cell types
set.seed(44)
pList <- c()
id <- sample(nrow(counts), size=1e4)
for(cc in 1:nlevels(celltype)){
curCT <- levels(celltype)[cc]
lowCT <- lowListGC[celltype == curCT]
dfList <- list()
for(ss in 1:length(lowCT)){
oox <- order(lowCT[[ss]]$x[id])
dfList[[ss]] <- data.frame(x=lowCT[[ss]]$x[id][oox], y=lowCT[[ss]]$y[id][oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
pCT <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("GC-content") +
ylab("Log(count + 1)") +
theme_classic() +
ggtitle(curCT) +
theme(legend.position = "none") +
ylim(c(0, 3.5))
pList[[cc]] <- pCT
}
cowplot::plot_grid(plotlist=pList, nrow=5, ncol=5)
## Warning: Removed 1 rows containing missing values (geom_path).
ggsave("~/Dropbox/research/atacseq/bulk/plots/gcEffectsAllCells.pdf",
units="in", width=12, height=9)
ggsave("~/Dropbox/research/atacseq/bulk/plots/gcEffectsAllCells.png",
units="in", width=12, height=9)
rm(lowListGC, lowCT, pList) ; gc()
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 10402581 555.6 17820628 951.8 NA 17820628 951.8
## Vcells 240769130 1837.0 1172395775 8944.7 16384 1465494718 11180.9
Peak width
lowListWidth <- list()
for(kk in 1:ncol(counts)){
lowListWidth[[kk]] <- lowess(x=log(width(gr)), y=log1p(counts[,kk]), f=1/10)
}
plot(x=seq(min(log(width(gr))), max(log(width(gr))), length=10),
y=seq(0, 5, length=10), type='n',
xlab="GC-content", ylab="Log(count + 1)")
for(kk in 1:length(lowListWidth)){
oo <- order(lowListWidth[[kk]]$x)
lines(x=lowListWidth[[kk]]$x[oo], y=lowListWidth[[kk]]$y[oo], col=colsBig[kk])
}
# for(cc in 1:nlevels(celltype)){
# curCT <- levels(celltype)[cc]
# id <- which(celltype == curCT)
# curBatch <- batch[id]
# plot(x=seq(min(log(width(gr))), max(log(width(gr))), length=10),
# y=seq(0, 5, length=10), type='n',
# xlab="GC-content", ylab="Log(count + 1)", main=curCT)
# for(ii in 1:length(id)){
# curID <- id[ii]
# oo <- order(lowListWidth[[curID]]$x)
# lines(x=lowListWidth[[curID]]$x[oo], y=lowListWidth[[curID]]$y[oo], col=colsBig[batch[curID]])
# }
# }
# for(bb in 1:nlevels(batch)){
# curB <- levels(batch)[bb]
# id <- which(batch == curB)
# plot(x=seq(min(log(width(gr))), max(log(width(gr))), length=10),
# y=seq(0, 5, length=10), type='n',
# xlab="GC-content", ylab="Log(count + 1)", main=curB)
# for(ii in 1:length(id)){
# curID <- id[ii]
# oo <- order(lowListWidth[[curID]]$x)
# lines(x=lowListWidth[[curID]]$x[oo], y=lowListWidth[[curID]]$y[oo], col=colsBig[batch[curID]])
# }
# }
# across all cell types
set.seed(44)
pList <- c()
id <- sample(nrow(counts), size=1e4)
for(cc in 1:nlevels(celltype)){
curCT <- levels(celltype)[cc]
lowCT <- lowListWidth[celltype == curCT]
dfList <- list()
for(ss in 1:length(lowCT)){
oox <- order(lowCT[[ss]]$x[id])
dfList[[ss]] <- data.frame(x=lowCT[[ss]]$x[id][oox], y=lowCT[[ss]]$y[id][oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
pCT <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("Log peak width") +
ylab("Log(count + 1)") +
theme_classic() +
ggtitle(curCT) +
theme(legend.position = "none") +
ylim(c(0, 4.5))
pList[[cc]] <- pCT
}
cowplot::plot_grid(plotlist=pList, nrow=5, ncol=5)
## Warning: Removed 9 rows containing missing values (geom_path).
## Warning: Removed 46 rows containing missing values (geom_path).
## Warning: Removed 12 rows containing missing values (geom_path).
## Warning: Removed 106 rows containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_path).
## Warning: Removed 19 rows containing missing values (geom_path).
## Warning: Removed 26 rows containing missing values (geom_path).
## Warning: Removed 154 rows containing missing values (geom_path).
ggsave("~/Dropbox/research/atacseq/bulk/plots/widthEffectsAllCells.pdf",
units="in", width=12, height=9)
ggsave("~/Dropbox/research/atacseq/bulk/plots/widthEffectsAllCells.png",
units="in", width=12, height=9)
rm(lowListWidth) ; gc()
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 10363831 553.5 17820628 951.8 NA 17820628 951.8
## Vcells 801852143 6117.7 1688425916 12881.7 16384 1688423219 12881.7
Mock comparisons
# Memory_NK cells
memContID <- celltype == "Memory_NK" & condition == "U"
countsMemControl <- counts[,memContID]
keepMemContr <- rowSums(cpm(countsMemControl) >= 2) >=3
countsMemControl <- countsMemControl[keepMemContr, ]
# these are all from different batches.
table(droplevels(batch[memContID]))
##
## 1001.0 1002.0 1004.0 1008.0 1010.0 1003.1
## 1 1 1 1 1 1
# equally sized bins after filtering
gcGroupsMem <- Hmisc::cut2(gcContentPeaks[keepMemContr], g=20)
gcMem <- gcContentPeaks[keepMemContr]
set.seed(33)
mock <- factor(sample(rep(letters[1:2], each=3)))
design <- model.matrix(~mock)
edgeR (TMM normalization)
## TMM normalization
library(edgeR)
d <- DGEList(countsMemControl)
d <- calcNormFactors(d)
d <- estimateDisp(d, design)
fit <- glmFit(d, design)
lrt <- glmLRT(fit, coef=2)
dfEdgeR <- data.frame(logFC=log(2^lrt$table$logFC),
gc=gcGroupsMem)
pedgeR <- ggplot(dfEdgeR) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("TMM normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
pedgeR
## Warning: Removed 185 rows containing non-finite values (stat_ydensity).
## Warning: Removed 185 rows containing non-finite values (stat_boxplot).
DESeq2 (MOR normalization)
## DESeq2 normalization
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countsMemControl,
colData=data.frame(mock=mock),
design=~mock)
dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.
## final dispersion estimates
## fitting model and testing
res <- results(dds, name="mock_b_vs_a")
dfDESeq2 <- data.frame(logFC=log(2^res$log2FoldChange),
gc=gcGroupsMem)
pdeseq <- ggplot(dfDESeq2) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("DESeq2 MOR normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
pdeseq
## Warning: Removed 249 rows containing non-finite values (stat_ydensity).
## Warning: Removed 249 rows containing non-finite values (stat_boxplot).
Full quantile
## Full quantile normalization
countsFQ <- FQnorm(countsMemControl, type="median")
d <- DGEList(countsFQ)
d <- estimateDisp(d, design)
fit <- glmFit(d, design)
lrtFQ <- glmLRT(fit, coef=2)
dfFQ <- data.frame(logFC=log(2^lrtFQ$table$logFC),
gc=gcGroupsMem)
pFQ <- ggplot(dfFQ) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("FQ normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
pFQ
## Warning: Removed 1001 rows containing non-finite values (stat_ydensity).
## Warning: Removed 1001 rows containing non-finite values (stat_boxplot).
Figure 2
##### FIGURE 2
## cqn
cqnModel <- cqn(countsMemControl, x=gcMem, sizeFactors = colSums(countsMemControl),
lengths=width(gr)[keepMemContr])
## Warning: The use of 'sig2' is deprecated; do specify 'sigma' (= sqrt(sig2))
## instead
d <- DGEList(countsMemControl)
d$offset <- cqnModel$glm.offset
d <- estimateDisp(d, design)
fit <- glmFit(d, design)
lrtCqn <- glmLRT(fit, coef=2)
dfCqn <- data.frame(logFC=log(2^lrtCqn$table$logFC),
gc=gcGroupsMem)
pCqn <- ggplot(dfCqn) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("cqn normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
# ## EDASeq
library(EDASeq)
## Loading required package: ShortRead
##
## Attaching package: 'ShortRead'
## The following object is masked from 'package:dplyr':
##
## id
## The following object is masked from 'package:purrr':
##
## compose
## The following object is masked from 'package:tibble':
##
## view
#emptyRows <- which(rownames(countsMouse) == "")
#rownames(countsMouse)[emptyRows] <- paste0("emptyPeak",1:length(emptyRows))
dataWithin <- withinLaneNormalization(countsMemControl, y=gcMem,
num.bins=20, which="full")
dataNorm <- betweenLaneNormalization(dataWithin, which="full")
d <- DGEList(dataNorm)
d <- estimateDisp(d, design)
## Warning: Zero sample variances detected, have been offset away from zero
fit <- glmFit(d, design)
lrtEDASeq <- glmLRT(fit, coef=2)
dfEDASeq <- data.frame(logFC=log(2^lrtEDASeq$table$logFC),
gc=gcGroupsMem)
pEDASeq <- ggplot(dfEDASeq) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("FQ-FQ normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
## GC-QN
countsGCQN <- gcqn(countsMemControl, gcGroupsMem, summary = "median")
d <- DGEList(countsGCQN)
d <- estimateDisp(d, design)
fit <- glmFit(d, design)
lrtGCQN <- glmLRT(fit, coef=2)
dfGCQN <- data.frame(logFC=log(2^lrtGCQN$table$logFC),
gc=gcGroupsMem)
pGCQN <- ggplot(dfGCQN) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("GC-FQ normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16))
## ridges plot before normalization
countsN <- countsMemControl[,order(colSums(countsMemControl), decreasing=TRUE)[1:3]]
for(ii in 1:ncol(countsN)){
lc <- log1p(countsN[,ii])
joyDat <- data.frame(lc=lc, gc=gcGroupsMem)
xLab <- base::ifelse(ii==2, "Log(count + 1)"," ")
yLab <- base::ifelse(ii==1, "GC-content bin"," ")
# axText <- ifelse(ii==1, 14, 0)
axText <- 0
ridgeHlp <- joyDat %>% ggplot(aes(y=gc)) +
geom_density_ridges(aes(x=lc)) +
theme_ridges(grid=FALSE, font_size=5, center_axis_labels = TRUE) +
xlim(c(0.5,7)) +
xlab(xLab) +
ylab(yLab) +
theme(axis.text.y = element_text(size=axText),
axis.text.x = element_text(size=10),
legend.position = "none",
axis.title = element_text(size=16))
assign(paste0("ridge",ii), ridgeHlp)
}
pRidge1 <- grid.arrange(ridge1,ridge2,ridge3, nrow=1)
## Picking joint bandwidth of 0.0792
## Warning: Removed 242 rows containing non-finite values (stat_density_ridges).
## Picking joint bandwidth of 0.165
## Warning: Removed 1276 rows containing non-finite values (stat_density_ridges).
## Picking joint bandwidth of 0.187
## Warning: Removed 1896 rows containing non-finite values (stat_density_ridges).
pFC <- cowplot::plot_grid(pCqn, pEDASeq, pGCQN,
labels=letters[2:4],
nrow=3, ncol=1)
## Warning: Removed 302 rows containing non-finite values (stat_ydensity).
## Warning: Removed 302 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1059 rows containing non-finite values (stat_ydensity).
## Warning: Removed 1059 rows containing non-finite values (stat_boxplot).
## Warning: Removed 905 rows containing non-finite values (stat_ydensity).
## Warning: Removed 905 rows containing non-finite values (stat_boxplot).
pFig2 <- cowplot::plot_grid(pRidge1,
pFC,
labels=c("a",""))
pFig2
ggsave("~/Dropbox/research/atacseq/bulk/plots/figure2.png",
units="in", width=12, height=9)
